A fast capacity estimation method based on open circuit voltage estimation for LiNixCoyMn1-x-y battery assessing in electric vehicles

Abstract With the widespread popularity of electric vehicles (EV), effective assessment for retired EVs has become increasingly critical. Unlike traditional internal combustion vehicles, for EV, batteries account for a large proportion of the entire vehicle cost. Therefore, a fast battery capacity estimation method based on open-circuit voltage (OCV) estimation is forthwith proposed. The method calculates capacity using the ratio of the change in electric quantity to the corresponding change in state-of-charge (SOC), and the SOC is estimated via a fast OCV estimation method proposed in this paper. The fast test procedure includes a charging/discharging test and a short rest, which take less than 30 minutes in total and provide the data for the battery capacity estimation. For estimation, a weighted voltage relaxation model, containing two parallel resistor–capacitor (RC) components, is established. Its parameters are then optimized using the beetle antenna search algorithm with the approximate OCV range obtained in the test and an early voltage relaxation curve. The results of the experiments show that the proposed model and algorithm can accurately estimate the OCV, and the capacity estimation can be quickly realized in half an hour while limiting inaccuracy to less than 3%.

[1]  Le Yi Wang,et al.  A capacity model based on charging process for state of health estimation of lithium ion batteries , 2016 .

[2]  Zonghai Chen,et al.  A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation , 2014 .

[3]  Jianqiu Li,et al.  Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles , 2014 .

[4]  Nuno Manoel Martins Dias Fouto,et al.  VALUATION OF QUALITY ATTRIBUTES IN THE PRICE OF NEW ECONOMY CARS , 2011 .

[5]  Languang Lu,et al.  Massive battery pack data compression and reconstruction using a frequency division model in battery management systems , 2020 .

[6]  Krishna R. Pattipati,et al.  Open circuit voltage characterization of lithium-ion batteries , 2014 .

[7]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[8]  Tatsuo Horiba,et al.  Capacity-fading prediction of lithium-ion batteries based on discharge curves analysis , 2011 .

[9]  Manu Vardhan,et al.  An adaptive inertia weight teaching-learning-based optimization algorithm and its applications , 2020 .

[10]  Yuejiu Zheng,et al.  Parameter sensitivity analysis and simplification of equivalent circuit model for the state of charge of lithium-ion batteries , 2020 .

[11]  Long Zhou,et al.  A hybrid state-of-charge estimation method based on credible increment for electric vehicle applications with large sensor and model errors , 2020 .

[12]  Zonghai Chen,et al.  A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles ☆ , 2017 .

[13]  Rui Xiong,et al.  Open circuit voltage and state of charge online estimation for lithium ion batteries , 2017 .

[14]  Hannah M. Dahn,et al.  User-Friendly Differential Voltage Analysis Freeware for the Analysis of Degradation Mechanisms in Li-Ion Batteries , 2012 .

[15]  Simona Onori,et al.  Aging and Characterization of Li-Ion Batteries in a HEV Application for Lifetime Estimation , 2010 .

[16]  Andrea Marongiu,et al.  Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles , 2015 .

[17]  Dylan Dah-Chuan Lu,et al.  Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries , 2018 .

[18]  Zonghai Chen,et al.  A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries , 2013 .

[19]  Sheldon S. Williamson,et al.  Power-Electronics-Based Solutions for Plug-in Hybrid Electric Vehicle Energy Storage and Management Systems , 2010, IEEE Transactions on Industrial Electronics.

[20]  Jianqiu Li,et al.  Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation , 2015 .

[21]  Dirk Uwe Sauer,et al.  Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination , 2013 .

[22]  Yi Xie,et al.  A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model , 2019, Applied Energy.

[23]  Chunbo Zhu,et al.  State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries , 2007, IEEE Transactions on Industrial Electronics.

[24]  Le Yi Wang,et al.  A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter , 2017 .

[25]  E. Barsoukov,et al.  Impedance spectroscopy : theory, experiment, and applications , 2005 .

[26]  Rudy R. Negenborn,et al.  Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks , 2019, Ocean Engineering.

[27]  Jun Teng,et al.  Beetle swarm optimisation for solving investment portfolio problems , 2018 .

[28]  Jianqiu Li,et al.  Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles , 2018 .

[29]  Chunbo Zhu,et al.  Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries , 2014 .

[30]  Amit Patra,et al.  State of Health Estimation of Lithium-Ion Batteries Using Capacity Fade and Internal Resistance Growth Models , 2018, IEEE Transactions on Transportation Electrification.

[31]  Yee-Pien Yang,et al.  Improved estimation of residual capacity of batteries for electric vehicles , 2008 .

[32]  Henk Jan Bergveld,et al.  Accuracy analysis of the state-of-charge and remaining run-time determination for lithium-ion batteries , 2009 .

[33]  Guojun Li,et al.  State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting , 2015 .

[34]  Yue Yuan,et al.  A novel efficient optimization algorithm for parameter estimation of building thermal dynamic models , 2019 .

[35]  Matthieu Dubarry,et al.  State-of-charge estimation and uncertainty for lithium-ion battery strings , 2014 .

[36]  Shuli Liu,et al.  An optimizer using the PSO algorithm to determine thermal parameters of PCM: A case study of grey water heat harnessing , 2019, International Journal of Heat and Mass Transfer.

[37]  M. Ouyang,et al.  State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter , 2018 .

[38]  Chunyun Fu,et al.  State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter , 2020, Applied Mathematical Modelling.

[39]  Zheng Chen,et al.  An online state of charge estimation method with reduced prior battery testing information , 2014 .

[40]  Gregory L. Plett,et al.  Recursive approximate weighted total least squares estimation of battery cell total capacity , 2011 .

[41]  Zhe Li,et al.  A review on the key issues of the lithium ion battery degradation among the whole life cycle , 2019, eTransportation.